The vision community has long sought to evolve representations of images or image patches that are quasi-invariant to transformations and to use them for a variety of problems like detection, recognition and tracking. The ability to establish correspondences between image patches under spatial deformations forms the basis of many approaches addressing machine vision problems.
In contrast to affine spatial deformations, not much focus has been directly targeted to the larger class (or a weaker constraint) of locally smooth deformations beyond the affine. Non-affine deformations may be caused by viewpoint changes under perspective projection, or transformations in imaged objects that are deformable or articulated. Object classes with large intraclass variations like bicycles, cars etc. can also be represented as archetypes with non-affine deformations. The application of non-affine deformations invariant image representations is thus very useful in machine vision, but is not widely applied or available. The application of non-affine deformations invariant image representations is desirable for highly precise online tracking of an object in video images.
Accordingly, improved and novel methods and systems that apply representations of image patches that are quasi-invariant to non-affine spatial deformations and that are applied in online tracking are required.